Overview

Dataset statistics

Number of variables10
Number of observations514
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.3 KiB
Average record size in memory80.2 B

Variable types

Numeric8
Categorical2

Warnings

link has a high cardinality: 461 distinct values High cardinality
name has a high cardinality: 459 distinct values High cardinality
age is highly correlated with amount_bricks and 1 other fieldsHigh correlation
amount_bricks is highly correlated with age and 1 other fieldsHigh correlation
price is highly correlated with age and 1 other fieldsHigh correlation
rating_amount is highly correlated with rating_worth and 1 other fieldsHigh correlation
rating_worth is highly correlated with rating_amount and 1 other fieldsHigh correlation
rating_fun is highly correlated with rating_amount and 1 other fieldsHigh correlation
age is highly correlated with amount_bricks and 1 other fieldsHigh correlation
amount_bricks is highly correlated with age and 1 other fieldsHigh correlation
price is highly correlated with age and 1 other fieldsHigh correlation
rating_amount is highly correlated with rating_worth and 1 other fieldsHigh correlation
rating_worth is highly correlated with rating_amount and 1 other fieldsHigh correlation
rating_fun is highly correlated with rating_amount and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with rating_amount and 2 other fieldsHigh correlation
age is highly correlated with rating_amount and 2 other fieldsHigh correlation
amount_bricks is highly correlated with price and 3 other fieldsHigh correlation
price is highly correlated with amount_bricks and 3 other fieldsHigh correlation
rating_amount is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
rating_worth is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
rating_fun is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
amount_bricks is highly correlated with price and 1 other fieldsHigh correlation
price is highly correlated with amount_bricks and 3 other fieldsHigh correlation
rating_amount is highly correlated with price and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with ageHigh correlation
product_number is highly correlated with priceHigh correlation
rating_fun is highly correlated with price and 2 other fieldsHigh correlation
age is highly correlated with amount_bricks and 1 other fieldsHigh correlation
rating_worth is highly correlated with rating_amount and 1 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
link is uniformly distributed Uniform
name is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
product_number has 21 (4.1%) zeros Zeros
price has 21 (4.1%) zeros Zeros
rating_amount has 450 (87.5%) zeros Zeros
rating_worth has 450 (87.5%) zeros Zeros
rating_fun has 450 (87.5%) zeros Zeros

Reproduction

Analysis started2021-09-20 14:16:25.148566
Analysis finished2021-09-20 14:17:37.528694
Duration1 minute and 12.38 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct514
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.5
Minimum0
Maximum513
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:37.649595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.65
Q1128.25
median256.5
Q3384.75
95-th percentile487.35
Maximum513
Range513
Interquartile range (IQR)256.5

Descriptive statistics

Standard deviation148.5232866
Coefficient of variation (CV)0.5790381544
Kurtosis-1.2
Mean256.5
Median Absolute Deviation (MAD)128.5
Skewness0
Sum131841
Variance22059.16667
MonotonicityStrictly increasing
2021-09-20T16:17:37.794658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.2%
3851
 
0.2%
3511
 
0.2%
3501
 
0.2%
3491
 
0.2%
3481
 
0.2%
3471
 
0.2%
3461
 
0.2%
3451
 
0.2%
3441
 
0.2%
Other values (504)504
98.1%
ValueCountFrequency (%)
01
0.2%
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
ValueCountFrequency (%)
5131
0.2%
5121
0.2%
5111
0.2%
5101
0.2%
5091
0.2%
5081
0.2%
5071
0.2%
5061
0.2%
5051
0.2%
5041
0.2%

link
Categorical

HIGH CARDINALITY
UNIFORM

Distinct461
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
https://www.lego.com/da-dk/product/batman-40386
 
3
https://www.lego.com/da-dk/product/the-joker-40428
 
3
https://www.lego.com/da-dk/product/app-controlled-top-gear-rally-car-42109
 
2
https://www.lego.com/da-dk/product/first-order-stormtrooper-40391
 
2
https://www.lego.com/da-dk/product/voldemort-nagini-bellatrix-40496
 
2
Other values (456)
502 

Length

Max length105
Median length61
Mean length61.92412451
Min length44

Characters and Unicode

Total characters31.829
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique410 ?
Unique (%)79.8%

Sample

1st rowhttps://www.lego.com/da-dk/product/empire-state-building-21046
2nd rowhttps://www.lego.com/da-dk/product/statue-of-liberty-21042
3rd rowhttps://www.lego.com/da-dk/product/taj-mahal-21056
4th rowhttps://www.lego.com/da-dk/product/trafalgar-square-21045
5th rowhttps://www.lego.com/da-dk/product/the-white-house-21054

Common Values

ValueCountFrequency (%)
https://www.lego.com/da-dk/product/batman-403863
 
0.6%
https://www.lego.com/da-dk/product/the-joker-404283
 
0.6%
https://www.lego.com/da-dk/product/app-controlled-top-gear-rally-car-421092
 
0.4%
https://www.lego.com/da-dk/product/first-order-stormtrooper-403912
 
0.4%
https://www.lego.com/da-dk/product/voldemort-nagini-bellatrix-404962
 
0.4%
https://www.lego.com/da-dk/product/belle-bottom-kevin-and-bob-404212
 
0.4%
https://www.lego.com/da-dk/product/disney-s-mickey-mouse-312022
 
0.4%
https://www.lego.com/da-dk/product/the-ice-castle-431972
 
0.4%
https://www.lego.com/da-dk/product/bruni-the-salamander-buildable-character-431862
 
0.4%
https://www.lego.com/da-dk/product/4x4-x-treme-off-roader-420992
 
0.4%
Other values (451)492
95.7%

Length

2021-09-20T16:17:38.085754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.lego.com/da-dk/product/batman-403863
 
0.6%
https://www.lego.com/da-dk/product/the-joker-404283
 
0.6%
https://www.lego.com/da-dk/product/app-controlled-top-gear-rally-car-421092
 
0.4%
https://www.lego.com/da-dk/product/first-order-stormtrooper-403912
 
0.4%
https://www.lego.com/da-dk/product/voldemort-nagini-bellatrix-404962
 
0.4%
https://www.lego.com/da-dk/product/belle-bottom-kevin-and-bob-404212
 
0.4%
https://www.lego.com/da-dk/product/disney-s-mickey-mouse-312022
 
0.4%
https://www.lego.com/da-dk/product/the-ice-castle-431972
 
0.4%
https://www.lego.com/da-dk/product/bruni-the-salamander-buildable-character-431862
 
0.4%
https://www.lego.com/da-dk/product/4x4-x-treme-off-roader-420992
 
0.4%
Other values (451)492
95.7%

Most occurring characters

ValueCountFrequency (%)
/2570
 
8.1%
-2286
 
7.2%
t2249
 
7.1%
o2218
 
7.0%
d1856
 
5.8%
w1643
 
5.2%
e1596
 
5.0%
c1422
 
4.5%
a1397
 
4.4%
r1306
 
4.1%
Other values (31)13286
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22730
71.4%
Other Punctuation4112
 
12.9%
Decimal Number2699
 
8.5%
Dash Punctuation2286
 
7.2%
Other Letter2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t2249
 
9.9%
o2218
 
9.8%
d1856
 
8.2%
w1643
 
7.2%
e1596
 
7.0%
c1422
 
6.3%
a1397
 
6.1%
r1306
 
5.7%
p1259
 
5.5%
s1122
 
4.9%
Other values (16)6662
29.3%
Decimal Number
ValueCountFrequency (%)
1507
18.8%
0377
14.0%
4317
11.7%
7277
10.3%
2248
9.2%
3218
8.1%
9200
 
7.4%
6197
 
7.3%
5192
 
7.1%
8166
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/2570
62.5%
.1028
 
25.0%
:514
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-2286
100.0%
Other Letter
ValueCountFrequency (%)
ǀ2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22732
71.4%
Common9097
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t2249
 
9.9%
o2218
 
9.8%
d1856
 
8.2%
w1643
 
7.2%
e1596
 
7.0%
c1422
 
6.3%
a1397
 
6.1%
r1306
 
5.7%
p1259
 
5.5%
s1122
 
4.9%
Other values (17)6664
29.3%
Common
ValueCountFrequency (%)
/2570
28.3%
-2286
25.1%
.1028
 
11.3%
:514
 
5.7%
1507
 
5.6%
0377
 
4.1%
4317
 
3.5%
7277
 
3.0%
2248
 
2.7%
3218
 
2.4%
Other values (4)755
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII31827
> 99.9%
Latin Ext B2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/2570
 
8.1%
-2286
 
7.2%
t2249
 
7.1%
o2218
 
7.0%
d1856
 
5.8%
w1643
 
5.2%
e1596
 
5.0%
c1422
 
4.5%
a1397
 
4.4%
r1306
 
4.1%
Other values (30)13284
41.7%
Latin Ext B
ValueCountFrequency (%)
ǀ2
100.0%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct459
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Batman™
 
3
Jokeren
 
3
Firhjulstrukket X-trem offroader
 
2
Daily Bugle
 
2
Joakim von And, Rip, Rap og Rup
 
2
Other values (454)
502 

Length

Max length67
Median length20
Mean length21.07003891
Min length3

Characters and Unicode

Total characters10.830
Distinct characters87
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique406 ?
Unique (%)79.0%

Sample

1st rowEmpire State Building
2nd rowFrihedsgudinden
3rd rowTaj Mahal
4th rowTrafalgar Square
5th rowDet Hvide Hus

Common Values

ValueCountFrequency (%)
Batman™3
 
0.6%
Jokeren3
 
0.6%
Firhjulstrukket X-trem offroader2
 
0.4%
Daily Bugle2
 
0.4%
Joakim von And, Rip, Rap og Rup2
 
0.4%
Voldemort™, Nagini og Bellatrix2
 
0.4%
Belle Bottom, Kevin og Bob2
 
0.4%
Gru, Stuart og Otto2
 
0.4%
Frost – isslot2
 
0.4%
Arendal slotsby2
 
0.4%
Other values (449)492
95.7%

Length

2021-09-20T16:17:38.392182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
og52
 
3.8%
33
 
2.4%
lego®28
 
2.0%
med17
 
1.2%
batman™13
 
0.9%
mod13
 
0.9%
klodser11
 
0.8%
mouse9
 
0.7%
mickey9
 
0.7%
i9
 
0.7%
Other values (798)1175
85.8%

Most occurring characters

ValueCountFrequency (%)
e1026
 
9.5%
850
 
7.8%
r755
 
7.0%
a645
 
6.0%
s601
 
5.5%
n587
 
5.4%
o575
 
5.3%
t571
 
5.3%
i513
 
4.7%
l418
 
3.9%
Other values (77)4289
39.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8282
76.5%
Uppercase Letter1261
 
11.6%
Space Separator855
 
7.9%
Dash Punctuation180
 
1.7%
Other Symbol109
 
1.0%
Decimal Number85
 
0.8%
Other Punctuation51
 
0.5%
Format3
 
< 0.1%
Other Letter2
 
< 0.1%
Initial Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1026
12.4%
r755
 
9.1%
a645
 
7.8%
s601
 
7.3%
n587
 
7.1%
o575
 
6.9%
t571
 
6.9%
i513
 
6.2%
l418
 
5.0%
d387
 
4.7%
Other values (21)2204
26.6%
Uppercase Letter
ValueCountFrequency (%)
M126
 
10.0%
B120
 
9.5%
S96
 
7.6%
E92
 
7.3%
A71
 
5.6%
G65
 
5.2%
H61
 
4.8%
K60
 
4.8%
P59
 
4.7%
L59
 
4.7%
Other values (17)452
35.8%
Decimal Number
ValueCountFrequency (%)
119
22.4%
916
18.8%
814
16.5%
211
12.9%
310
11.8%
08
9.4%
53
 
3.5%
72
 
2.4%
61
 
1.2%
41
 
1.2%
Other Punctuation
ValueCountFrequency (%)
,21
41.2%
:14
27.5%
.6
 
11.8%
/3
 
5.9%
'2
 
3.9%
"2
 
3.9%
!1
 
2.0%
&1
 
2.0%
#1
 
2.0%
Space Separator
ValueCountFrequency (%)
850
99.4%
 5
 
0.6%
Other Symbol
ValueCountFrequency (%)
73
67.0%
®36
33.0%
Dash Punctuation
ValueCountFrequency (%)
-147
81.7%
33
 
18.3%
Format
ValueCountFrequency (%)
3
100.0%
Other Letter
ValueCountFrequency (%)
ǀ2
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9545
88.1%
Common1285
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1026
 
10.7%
r755
 
7.9%
a645
 
6.8%
s601
 
6.3%
n587
 
6.1%
o575
 
6.0%
t571
 
6.0%
i513
 
5.4%
l418
 
4.4%
d387
 
4.1%
Other values (49)3467
36.3%
Common
ValueCountFrequency (%)
850
66.1%
-147
 
11.4%
73
 
5.7%
®36
 
2.8%
33
 
2.6%
,21
 
1.6%
119
 
1.5%
916
 
1.2%
814
 
1.1%
:14
 
1.1%
Other values (18)62
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10526
97.2%
Latin 1 Sup191
 
1.8%
Letterlike Symbols73
 
0.7%
Punctuation38
 
0.4%
Latin Ext B2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1026
 
9.7%
850
 
8.1%
r755
 
7.2%
a645
 
6.1%
s601
 
5.7%
n587
 
5.6%
o575
 
5.5%
t571
 
5.4%
i513
 
4.9%
l418
 
4.0%
Other values (63)3985
37.9%
Letterlike Symbols
ValueCountFrequency (%)
73
100.0%
Punctuation
ValueCountFrequency (%)
33
86.8%
3
 
7.9%
1
 
2.6%
1
 
2.6%
Latin 1 Sup
ValueCountFrequency (%)
æ55
28.8%
ø48
25.1%
å40
20.9%
®36
18.8%
 5
 
2.6%
Æ4
 
2.1%
á2
 
1.0%
é1
 
0.5%
Latin Ext B
ValueCountFrequency (%)
ǀ2
100.0%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.642023346
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:38.514877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q310
95-th percentile18
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.429682861
Coefficient of variation (CV)0.5125747389
Kurtosis0.09835324228
Mean8.642023346
Median Absolute Deviation (MAD)2
Skewness0.9736949455
Sum4442
Variance19.62209025
MonotonicityNot monotonic
2021-09-20T16:17:38.607156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
694
18.3%
889
17.3%
765
12.6%
1858
11.3%
1045
8.8%
438
7.4%
928
 
5.4%
1625
 
4.9%
525
 
4.9%
221
 
4.1%
Other values (5)26
 
5.1%
ValueCountFrequency (%)
14
 
0.8%
221
 
4.1%
33
 
0.6%
438
7.4%
525
 
4.9%
694
18.3%
765
12.6%
889
17.3%
928
 
5.4%
1045
8.8%
ValueCountFrequency (%)
1858
11.3%
1625
 
4.9%
145
 
1.0%
129
 
1.8%
115
 
1.0%
1045
8.8%
928
 
5.4%
889
17.3%
765
12.6%
694
18.3%

amount_bricks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean833.9338521
Minimum1
Maximum11695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:38.724304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.65
Q1118.25
median386.5
Q3973.25
95-th percentile3318.25
Maximum11695
Range11694
Interquartile range (IQR)855

Descriptive statistics

Standard deviation1246.781419
Coefficient of variation (CV)1.495060328
Kurtosis17.6695095
Mean833.9338521
Median Absolute Deviation (MAD)304.5
Skewness3.416114376
Sum428642
Variance1554463.906
MonotonicityNot monotonic
2021-09-20T16:17:38.869260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118
 
3.5%
5010
 
1.9%
906
 
1.2%
335
 
1.0%
345
 
1.0%
364
 
0.8%
2344
 
0.8%
734
 
0.8%
5654
 
0.8%
4664
 
0.8%
Other values (356)450
87.5%
ValueCountFrequency (%)
118
3.5%
72
 
0.4%
83
 
0.6%
103
 
0.6%
112
 
0.4%
131
 
0.2%
171
 
0.2%
181
 
0.2%
232
 
0.4%
242
 
0.4%
ValueCountFrequency (%)
116951
0.2%
90361
0.2%
75411
0.2%
60201
0.2%
56851
0.2%
55441
0.2%
55091
0.2%
51001
0.2%
47841
0.2%
42492
0.4%

product_number
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct444
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.560528431 × 1018
Minimum0
Maximum2.0004312 × 1020
Zeros21
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:39.156260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.027140271 × 1014
Q13.1100061 × 1014
median4.209792098 × 1014
Q37.531150312 × 1014
95-th percentile8.001615016 × 1014
Maximum2.0004312 × 1020
Range2.0004312 × 1020
Interquartile range (IQR)4.421144211 × 1014

Descriptive statistics

Standard deviation1.759502963 × 1019
Coefficient of variation (CV)11.27504586
Kurtosis124.7271812
Mean1.560528431 × 1018
Median Absolute Deviation (MAD)2.893228932 × 1014
Skewness11.23568835
Sum8.021116137 × 1020
Variance3.095850678 × 1038
MonotonicityNot monotonic
2021-09-20T16:17:39.301563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021
 
4.1%
4.038640386 × 10143
 
0.6%
4.042840428 × 10143
 
0.6%
4.04204042 × 10142
 
0.4%
4.037740377 × 10142
 
0.4%
4.049640496 × 10142
 
0.4%
4.042140421 × 10142
 
0.4%
4.317243172 × 10142
 
0.4%
2.132621326 × 10142
 
0.4%
4.2100421 × 10142
 
0.4%
Other values (434)473
92.0%
ValueCountFrequency (%)
021
4.1%
1.025510255 × 10141
 
0.2%
1.026110261 × 10141
 
0.2%
1.026410264 × 10141
 
0.2%
1.026510265 × 10141
 
0.2%
1.02701027 × 10141
 
0.2%
1.027210272 × 10141
 
0.2%
1.027310273 × 10142
 
0.4%
1.027410274 × 10141
 
0.2%
1.027610276 × 10141
 
0.2%
ValueCountFrequency (%)
2.0004312 × 10201
0.2%
2.0004302 × 10201
0.2%
2.0004142 × 10201
0.2%
2.0004092 × 10201
0.2%
8.540658541 × 10171
0.2%
8.538658539 × 10171
0.2%
9.217792178 × 10141
0.2%
9.217692177 × 10141
0.2%
8.801588016 × 10141
0.2%
8.801488015 × 10141
0.2%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct80
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean656.151751
Minimum0
Maximum6999
Zeros21
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:39.441758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1180
median354
Q3899
95-th percentile2134
Maximum6999
Range6999
Interquartile range (IQR)719

Descriptive statistics

Standard deviation790.5202203
Coefficient of variation (CV)1.204782612
Kurtosis12.84975148
Mean656.151751
Median Absolute Deviation (MAD)254
Skewness2.900690392
Sum337262
Variance624922.2186
MonotonicityNot monotonic
2021-09-20T16:17:39.572694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18049
 
9.5%
27029
 
5.6%
10027
 
5.3%
44926
 
5.1%
13026
 
5.1%
89925
 
4.9%
54922
 
4.3%
021
 
4.1%
34920
 
3.9%
64920
 
3.9%
Other values (70)249
48.4%
ValueCountFrequency (%)
021
4.1%
306
 
1.2%
407
 
1.4%
501
 
0.2%
608
 
1.6%
703
 
0.6%
802
 
0.4%
10027
5.3%
1041
 
0.2%
13026
5.1%
ValueCountFrequency (%)
69991
0.2%
52991
0.2%
43501
0.2%
41601
0.2%
38991
0.2%
34991
0.2%
33992
0.4%
31992
0.4%
31101
0.2%
29991
0.2%

rating_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5206225681
Minimum0
Maximum4.9
Zeros450
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:39.690226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.4
Maximum4.9
Range4.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.393497431
Coefficient of variation (CV)2.676598204
Kurtosis3.600363366
Mean0.5206225681
Median Absolute Deviation (MAD)0
Skewness2.344308308
Sum267.6
Variance1.941835089
MonotonicityNot monotonic
2021-09-20T16:17:39.801488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0450
87.5%
4.48
 
1.6%
4.57
 
1.4%
47
 
1.4%
4.67
 
1.4%
4.26
 
1.2%
3.95
 
1.0%
4.35
 
1.0%
4.75
 
1.0%
3.73
 
0.6%
Other values (8)11
 
2.1%
ValueCountFrequency (%)
0450
87.5%
2.22
 
0.4%
3.11
 
0.2%
3.31
 
0.2%
3.51
 
0.2%
3.61
 
0.2%
3.73
 
0.6%
3.95
 
1.0%
47
 
1.4%
4.13
 
0.6%
ValueCountFrequency (%)
4.91
 
0.2%
4.81
 
0.2%
4.75
1.0%
4.67
1.4%
4.57
1.4%
4.48
1.6%
4.35
1.0%
4.26
1.2%
4.13
 
0.6%
47
1.4%

rating_worth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5145914397
Minimum0
Maximum4.8
Zeros450
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:39.899693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.3
Maximum4.8
Range4.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.379564417
Coefficient of variation (CV)2.680892667
Kurtosis3.650945506
Mean0.5145914397
Median Absolute Deviation (MAD)0
Skewness2.353587991
Sum264.5
Variance1.903197981
MonotonicityNot monotonic
2021-09-20T16:17:40.004077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0450
87.5%
4.410
 
1.9%
4.18
 
1.6%
4.38
 
1.6%
4.26
 
1.2%
3.95
 
1.0%
4.75
 
1.0%
3.84
 
0.8%
4.64
 
0.8%
4.83
 
0.6%
Other values (9)11
 
2.1%
ValueCountFrequency (%)
0450
87.5%
1.81
 
0.2%
2.21
 
0.2%
2.81
 
0.2%
31
 
0.2%
3.41
 
0.2%
3.62
 
0.4%
3.71
 
0.2%
3.84
 
0.8%
3.95
 
1.0%
ValueCountFrequency (%)
4.83
 
0.6%
4.75
1.0%
4.64
 
0.8%
4.52
 
0.4%
4.410
1.9%
4.38
1.6%
4.26
1.2%
4.18
1.6%
41
 
0.2%
3.95
1.0%

rating_fun
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5206225681
Minimum0
Maximum4.9
Zeros450
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-09-20T16:17:40.102313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.4
Maximum4.9
Range4.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.393497431
Coefficient of variation (CV)2.676598204
Kurtosis3.600363366
Mean0.5206225681
Median Absolute Deviation (MAD)0
Skewness2.344308308
Sum267.6
Variance1.941835089
MonotonicityNot monotonic
2021-09-20T16:17:40.204972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0450
87.5%
4.48
 
1.6%
4.57
 
1.4%
47
 
1.4%
4.67
 
1.4%
4.26
 
1.2%
3.95
 
1.0%
4.35
 
1.0%
4.75
 
1.0%
3.73
 
0.6%
Other values (8)11
 
2.1%
ValueCountFrequency (%)
0450
87.5%
2.22
 
0.4%
3.11
 
0.2%
3.31
 
0.2%
3.51
 
0.2%
3.61
 
0.2%
3.73
 
0.6%
3.95
 
1.0%
47
 
1.4%
4.13
 
0.6%
ValueCountFrequency (%)
4.91
 
0.2%
4.81
 
0.2%
4.75
1.0%
4.67
1.4%
4.57
1.4%
4.48
1.6%
4.35
1.0%
4.26
1.2%
4.13
 
0.6%
47
1.4%

Interactions

2021-09-20T16:16:31.052337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:31.188091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:31.294280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:31.403021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:33.492301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:33.599942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:33.701102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:33.804850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:33.929044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:34.034685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:34.142769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:34.369240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:36.456867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:36.567807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:36.673151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:36.777114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:36.881029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:36.985678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:37.092444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:37.202001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:39.400421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:39.511552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:39.617774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:39.724489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:39.832991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:44.614359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:49.288429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:16:54.705885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:02.583908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:07.990330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:12.725758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:19.132199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:25.070953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:25.385724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:25.518937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:25.663718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:27.811497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:27.927713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:28.033830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:28.140157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:28.246864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:28.356390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:28.459309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:28.565463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:30.787760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:30.892804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:30.992800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:31.092767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:31.195064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:31.304251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:31.406599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:31.512221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:33.745600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:33.854724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:33.977820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:34.077472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:34.177439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:34.285582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:34.389677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:34.495334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:36.728002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:36.837293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:36.937329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-20T16:17:37.037124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-09-20T16:17:40.309265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-20T16:17:40.462049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-20T16:17:40.602842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-20T16:17:40.753239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-20T16:17:37.225875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-20T16:17:37.413790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0linknameageamount_bricksproduct_numberpricerating_amountrating_worthrating_fun
00https://www.lego.com/da-dk/product/empire-state-building-21046Empire State Building1617672104621046210468990.00.00.0
11https://www.lego.com/da-dk/product/statue-of-liberty-21042Frihedsgudinden1616852104221042210428990.00.00.0
22https://www.lego.com/da-dk/product/taj-mahal-21056Taj Mahal1820222105621056210568990.00.00.0
33https://www.lego.com/da-dk/product/trafalgar-square-21045Trafalgar Square1211972104521045210457490.00.00.0
44https://www.lego.com/da-dk/product/the-white-house-21054Det Hvide Hus1814832105421054210547190.00.00.0
55https://www.lego.com/da-dk/product/tokyo-21051Tokyo165472105121051210515490.00.00.0
66https://www.lego.com/da-dk/product/new-york-city-21028New York City125982102821028210284490.00.00.0
77https://www.lego.com/da-dk/product/paris-21044Paris126492104421044210444490.00.00.0
88https://www.lego.com/da-dk/product/london-21034London124682103421034210343994.04.14.0
99https://www.lego.com/da-dk/product/dubai-21052Dubai167402105221052210525490.00.00.0

Last rows

Unnamed: 0linknameageamount_bricksproduct_numberpricerating_amountrating_worthrating_fun
504504https://www.lego.com/da-dk/product/punk-pirate-beatbox-43103Punk Pirate BeatBox7734310343103431031800.00.00.0
505505https://www.lego.com/da-dk/product/unicorn-dj-beatbox-43106Unicorn DJ BeatBox7844310643106431061800.00.00.0
506506https://www.lego.com/da-dk/product/bandmates-43101Bandmates711431014310143101400.00.00.0
507507https://www.lego.com/da-dk/product/water-tape-854065Vandbånd623854065854065854065800.00.00.0
508508https://www.lego.com/da-dk/product/lego-xtra-sea-accessories-40341LEGO® xtra havudstyr624403414034140341400.00.00.0
509509https://www.lego.com/da-dk/product/botanical-accessories-40376Plantetilbehør632403764037640376400.00.00.0
510510https://www.lego.com/da-dk/product/sports-accessories-40375Sportstilbehør636403754037540375400.00.00.0
511511https://www.lego.com/da-dk/product/xtra-chinatown-40464xtra Chinatown835404644046440464400.00.00.0
512512https://www.lego.com/da-dk/product/xtra-food-40465xtra Mad636404654046540465400.00.00.0
513513https://www.lego.com/da-dk/product/food-accessories-40309Madtilbehør630403094030940309400.00.00.0